Delta Alarm Classification and Ranking System
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Solution Overview
Problem
Manual inspection of delta alarms generated during source code analysis is costly, tedious, and error-prone due to the large number of alarms and lack of prioritization based on code changes, leading to inefficient identification of potential errors.
Innovation Solution
A processor-implemented method and system for source code analysis that computes delta alarms by mapping previous and current source code versions, classifying newly generated and impacted alarms based on code changes, and assigning priorities to classify and rank delta alarms for improved reporting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If delta alarms computation techniques are used to reduce the number of alarms, then the quantity of alarms is reduced, but the difficulty of detecting and measuring increases due to lack of prioritization and classification
Solution Approach 1:
The patent segments delta alarms into multiple priority levels (high, medium, low) and categories (newly generated, repeated, suppressed) based on code change impact analysis. This segmentation transforms a single undifferentiated list of alarms into a structured hierarchy that guides manual inspection effort toward the most critical issues first, reducing the effective difficulty of detection despite the large total number of alarms.
Solution Approach 2:
The patent introduces new parameters for alarm characterization including priority level, alarm category (newly generated vs. repeated), and impact type (data dependency, control dependency, value dependency). These parameter changes enable automated prioritization and classification of alarms, transforming the inspection process from unguided manual review to targeted analysis based on multiple discriminating parameters.
2Reliability
If manual inspection of all delta alarms is performed to ensure accurate defect identification, then the reliability of defect detection is improved, but the loss of time and productivity increases significantly
Solution Approach 1:
The patent performs preliminary automated actions including code mapping between versions, identification of impacting code changes, computation of data/control/value dependencies, and assignment of priority levels and categories to each alarm. This preliminary processing prepares the alarm data in advance, so that when manual inspection occurs, inspectors can focus only on high-priority alarms with pre-analyzed context, significantly reducing the time required while maintaining reliability through the thoroughness of automated preprocessing.
Solution Approach 2:
The patent implements feedback mechanisms where inspection results are used to refine and update the alarm prioritization model. As inspectors validate or dismiss alarms, this feedback improves the accuracy of future prioritization, creating a learning system that becomes increasingly efficient over time while maintaining high reliability through continuous validation.
3Measurement precision
If comprehensive analysis of code changes is performed to determine alarm impact, then the measurement precision of alarm relevance is improved, but the device complexity increases due to multiple analysis techniques required
Solution Approach 1:
The patent segments the complex code change analysis into three distinct dependency types: data dependencies (variable value flows), control dependencies (execution path flows), and value dependencies (computed results). Each dependency type is analyzed using specialized techniques appropriate to its nature, allowing the system to achieve comprehensive precision without overwhelming complexity. Each dependency category has its own analysis methodology and impact assessment criteria.
Solution Approach 2:
The patent introduces code mapping as an intermediary structure that connects previous version code to current version code. This mapping serves as a mediator that enables precise tracking of how code changes propagate through the codebase and affect specific alarm conditions. The mapping structure organizes the complexity of cross-version analysis into a manageable format that facilitates precise impact assessment without requiring direct complex analysis of all code transformations.
Data Source
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AI summary
Source code of any application may be edited/modified to accommodate new changes. The changes in the source code may also affect static analysis alarms that were generated for the original source code. Changes in the source code may result in newly generated alarms, some of the alarms in the original source code may repeat in the new source code. Many of the repeated alarms get suppressed using appropriate techniques. The repeated alarms that remain after the suppression, and the newly generated alarms together form the delta alarms. Each of the delta alarms may have been generated due to different reasons. The method and system disclosed herein permits classification of the delta alarms, based on reason/cause of each of the alarms. The system further performs ranking of the classes of the delta alarms. Further, the system groups the alarms having common cause and reports the delta alarms with their causes.